# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import unittest

import numpy as np
from transformers import AutoTokenizer

import mindspore as ms
from mindspore import mint

from mindone.diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanPipeline, WanTransformer3DModel
from mindone.diffusers.utils.testing_utils import floats_tensor
from mindone.transformers import T5EncoderModel

sys.path.append(".")

from utils import PeftLoraLoaderMixinTests  # noqa: E402


class WanLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
    pipeline_class = WanPipeline
    scheduler_cls = FlowMatchEulerDiscreteScheduler
    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
    scheduler_kwargs = {}

    transformer_kwargs = {
        "patch_size": (1, 2, 2),
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "in_channels": 16,
        "out_channels": 16,
        "text_dim": 32,
        "freq_dim": 256,
        "ffn_dim": 32,
        "num_layers": 2,
        "cross_attn_norm": True,
        "qk_norm": "rms_norm_across_heads",
        "rope_max_seq_len": 32,
    }
    transformer_cls = WanTransformer3DModel
    vae_kwargs = {
        "base_dim": 3,
        "z_dim": 16,
        "dim_mult": [1, 1, 1, 1],
        "num_res_blocks": 1,
        "temperal_downsample": [False, True, True],
    }
    vae_cls = AutoencoderKLWan
    has_two_text_encoders = True
    tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
    text_encoder_cls, text_encoder_id, text_encoder_revision = (
        T5EncoderModel,
        "hf-internal-testing/tiny-random-t5",
        "refs/pr/1",
    )

    text_encoder_target_modules = ["q", "k", "v", "o"]

    @property
    def output_shape(self):
        return (1, 9, 32, 32, 3)

    def get_dummy_inputs(self, with_generator=True):
        batch_size = 1
        sequence_length = 16
        num_channels = 4
        num_frames = 9
        num_latent_frames = 3  # (num_frames - 1) // temporal_compression_ratio + 1
        sizes = (4, 4)

        generator = np.random.default_rng(0)
        noise = floats_tensor((batch_size, num_latent_frames, num_channels) + sizes)
        input_ids = mint.randint(1, sequence_length, (batch_size, sequence_length), generator=ms.manual_seed(0))

        pipeline_inputs = {
            "prompt": "",
            "num_frames": num_frames,
            "num_inference_steps": 1,
            "guidance_scale": 6.0,
            "height": 32,
            "width": 32,
            "max_sequence_length": sequence_length,
            "output_type": "np",
        }
        if with_generator:
            pipeline_inputs.update({"generator": generator})

        return noise, input_ids, pipeline_inputs

    def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
        super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)

    def test_simple_inference_with_text_denoiser_lora_unfused(self):
        super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)

    @unittest.skip("Not supported in Wan.")
    def test_simple_inference_with_text_denoiser_block_scale(self):
        pass

    @unittest.skip("Not supported in Wan.")
    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
        pass

    @unittest.skip("Not supported in Wan.")
    def test_modify_padding_mode(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Wan.")
    def test_simple_inference_with_partial_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Wan.")
    def test_simple_inference_with_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Wan.")
    def test_simple_inference_with_text_lora_and_scale(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Wan.")
    def test_simple_inference_with_text_lora_fused(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in Wan.")
    def test_simple_inference_with_text_lora_save_load(self):
        pass
